Author

Claire Wilson

University of LeicesterORCID 0000-0002-6401-2014Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:deep learning· 3 Organism:human· 3 Assay:imaging· 3 Research Focus:cell type annotation· 2 Research Focus:cell-cell communication· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:spatial domain detection· 1

Frequent Co-Authors

Fiona BallantyneiD· 3 shared Ian R. PowleyiD· 3 shared John P. C. Le QuesneiD· 3 shared Kai RakoviciD· 3 shared Ke YuaniD· 3 shared Leah Officer-JonesiD· 3 shared Marco SerenoiD· 3 shared Adalberto Claudio QuirosiD· 2 shared

Activity by Year

2025-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersCancer Biology

Fiona BallantyneORCID

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3 papers

Recent·Most cited

Self-supervised AI reveals a lethal discohesive phenotype in lung adenocarcinoma

Kai Rakovic, Alexandrina Pancheva, Adalberto Claudio Quiros, Jose Coelho‐Lima, Zhangyi He, David A. Dorward, Marco Sereno, Claire Wilson, Craig Dick, David Allan Moore, Fiona Ballantyne, Catherine Ficken, Ana Teodòsio, Silvia Martinelli, Rachel Baird, , John P. C. Le Quesne

This paper uses self-supervised AI to identify under-appreciated stromal morphologies in lung adenocarcinoma linked to poor prognosis, independent of current clinical schemas.

bioRxiv · Nov 12, 2025 · PreprintCancer Biology

Self-Supervised AI Reveals a Hidden Landscape of Prognostic Spatial Patterns in Multiplex Immunofluorescence Images

Lucas Farndale, Kai Rakovic, Ian R. Powley, Phimmada Hatthakarnkul, Farzaneh Alsadat Seyedshahi, Sona Jesenakova, Leonor Patricia Schubert Santana, Silvia Martinelli, Fiona Ballantyne, Leah Officer-Jones, David Moore, Claire Wilson, Noori Maka, Campbell S.D. Roxburgh, Crispin J. Miller, , Marco Sereno

This paper introduces a self-supervised learning framework to analyze multiplex immunofluorescence images, revealing novel prognostic spatial patterns in cancer tissues.

bioRxiv · Oct 16, 2025 · PreprintCancer Biology

A histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images

Farzaneh Seyedshahi, Kai Rakovic, Nicolas Poulain, Adalberto Claudio Quiros, Ian R. Powley, Cathy J. Richards, Hussein Uraiby, Sonja Klebe, David Allan Moore, Apostolos Nakas, Claire Wilson, Marco Sereno, Leah Officer-Jones, Catherine Ficken, Ana Teodòsio, , John P. C. Le Quesne

This paper uses self-supervised learning on 3446 H&E-stained mesothelioma slides to create a histomorphological atlas, achieving high predictive performance and revealing molecular insights.

Nature Communications · Oct 7, 2025Peer-reviewedPathology